A Combined Unmixing Framework for Impervious Surface Mapping on Medium-Resolution Images with Visible Shadows

Spectral unmixing methods with medium-resolution remote sensing images have become the main approach to mapping urban impervious-surface information. However, as more tall buildings appear, numerous visible shadows exist in medium-resolution images; these have usually been ignored in previous research, but they seriously affect accuracy. To solve this problem, we propose a combined unmixing framework to extract impervious surface in nonshadow and shadow areas, using linear and nonlinear unmixing models, respectively. First shadow is separated from nonshadow. Then a nonlinear unmixing method is selected to map impervious surface in shadow, which is more suitable to the complex imaging environment in shadow, and a classic linear unmixing model in nonshadow. Through experimental tests, the proposed combined unmixing framework is shown to effectively reduce error in two study areas compared with classical unmixing methods.

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Kernel-Based Nonlinear Spectral Unmixing with Dictionary Pruning

Remote Sensing ◽

10.3390/rs11050529 ◽

2019 ◽

Vol 11 (5) ◽

pp. 529

Author(s):

Zeng Li ◽

Jie Chen ◽

Susanto Rahardja

Keyword(s):

Real Data ◽

Specific Model ◽

Spectral Unmixing ◽

Spectral Library ◽

Model Assumption ◽

Linear Unmixing ◽

Complex Scenes ◽

Fluctuation Model ◽

Nonlinear Fluctuation ◽

Unmixing Model

Spectral unmixing extracts subpixel information by decomposing observed pixel spectra into a collection of constituent spectra signatures and their associated fractions. Considering the restriction of linear unmixing model, nonlinear unmixing algorithms find their applications in complex scenes. Kernel-based algorithms serve as important candidates for nonlinear unmixing as they do not require specific model assumption and have moderate computational complexity. In this paper we focus on the linear mixture and nonlinear fluctuation model. We propose a two-step kernel-based unmixing algorithm to address the case where a large spectral library is used as the candidate endmembers or the sparse mixture case. The sparsity-inducing regularization is introduced to perform the endmember selection and the candidate library is then pruned to provide more accurate results. Experimental results with synthetic and real data, particularly those laboratory-created labeled, show the effectiveness of the proposed algorithm compared with state-of-art methods.

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Stratified spectral mixture analysis of medium resolution imagery for impervious surface mapping

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2017.04.006 ◽

2017 ◽

Vol 60 ◽

pp. 38-48 ◽

Cited By ~ 12

Author(s):

Genyun Sun ◽

Xiaolin Chen ◽

Jinchang Ren ◽

Aizhu Zhang ◽

Xiuping Jia

Keyword(s):

Impervious Surface ◽

Spectral Mixture Analysis ◽

Mixture Analysis ◽

Surface Mapping ◽

Medium Resolution ◽

Spectral Mixture

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An on-demand scheme driven by the knowledge of geospatial distribution for large-scale high-resolution impervious surface mapping

GIScience & Remote Sensing ◽

10.1080/15481603.2021.1909304 ◽

2021 ◽

pp. 1-25

Author(s):

Min Huang ◽

Nengcheng Chen ◽

Wenying Du ◽

Mengtian Wen ◽

Daoye Zhu ◽

...

Keyword(s):

High Resolution ◽

Large Scale ◽

Impervious Surface ◽

Surface Mapping ◽

On Demand

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Algoritmo de Descomposición Ciega Basado en el Modelo de Mezcla Multi-Lineal

10.24254/cnib.21.13 ◽

2021 ◽

Author(s):

◽

J. N. Mendoza Chavarría

Keyword(s):

State Of The Art ◽

Hyperspectral Data ◽

Spectral Unmixing ◽

Mixing Models ◽

Nonlinear Interactions ◽

Estimation Errors ◽

Spectral Signatures ◽

Scattering Effects ◽

Linear Unmixing ◽

Multiple Light Scattering

Spectral unmixing has proven to be a great tool for the analysis of hyperspectral data, with linear mixing models (LMMs) being the most used in the literature. Nevertheless, due to the limitations of the LMMs to accurately describe the multiple light scattering effects in multi and hyperspectral imaging, new mixing models have emerged to describe nonlinear interactions. In this paper, we propose a new nonlinear unmixing algorithm based on a multilinear mixture model called Non-linear Extended Blind Endmember and Abundance Extraction (NEBEAE), which is based on a linear unmixing method established in the literature. The results of this study show that proposed method decreases the estimation errors of the spectral signatures and abundance maps, as well as the execution time with respect the state of the art methods.

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An Edge-Sense Bidirectional Pyramid Network for Stereo Matching of VHR Remote Sensing Images

Remote Sensing ◽

10.3390/rs12244025 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4025

Author(s):

Rongshu Tao ◽

Yuming Xiang ◽

Hongjian You

Keyword(s):

Remote Sensing ◽

Stereo Matching ◽

Tall Buildings ◽

Disparity Estimation ◽

Complex Structures ◽

Learning Networks ◽

Remote Sensing Images ◽

Essential Step ◽

Disparity Range ◽

The Cost

As an essential step in 3D reconstruction, stereo matching still faces unignorable problems due to the high resolution and complex structures of remote sensing images. Especially in occluded areas of tall buildings and textureless areas of waters and woods, precise disparity estimation has become a difficult but important task. In this paper, we develop a novel edge-sense bidirectional pyramid stereo matching network to solve the aforementioned problems. The cost volume is constructed from negative to positive disparities since the disparity range in remote sensing images varies greatly and traditional deep learning networks only work well for positive disparities. Then, the occlusion-aware maps based on the forward-backward consistency assumption are applied to reduce the influence of the occluded area. Moreover, we design an edge-sense smoothness loss to improve the performance of textureless areas while maintaining the main structure. The proposed network is compared with two baselines. The experimental results show that our proposed method outperforms two methods, DenseMapNet and PSMNet, in terms of averaged endpoint error (EPE) and the fraction of erroneous pixels (D1), and the improvements in occluded and textureless areas are significant.

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